US 12,340,346 B1
Consumer engagement and management platform using machine learning for intent driven orchestration
J. Wayne Kullman, Glenview, IL (US); Thomas Pearson, Northbrook, IL (US); Dan Maloney, Northbrook, IL (US); Vinith George, Northbrook, IL (US); and Natalie Merlo, Northbrook, IL (US)
Assigned to Allstate Insurance Company, Northbrook, IL (US)
Filed by Allstate Insurance Company, Northbrook, IL (US)
Filed on Jul. 26, 2021, as Appl. No. 17/385,315.
Claims priority of provisional application 63/056,791, filed on Jul. 27, 2020.
Int. Cl. G06Q 10/10 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06Q 30/01 (2023.01); G06Q 30/0283 (2023.01); G06Q 40/08 (2012.01)
CPC G06Q 10/10 (2013.01) [G06F 18/2155 (2023.01); G06N 20/00 (2019.01); G06Q 30/01 (2013.01); G06Q 30/0283 (2013.01); G06Q 40/08 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive historical data from a plurality of sources comprising at least an internal data source of the computing platform and an external data source external from and communicatively coupled to the computing platform, wherein the historical data corresponds to an individual;
identify data corresponding to the individual using one or more intent orchestration models trained using the historical data that corresponds to the individual, wherein the one or more intent orchestration models include a plurality of intent identification models, one or more engagement output generation modules, and one or more communication channel modules;
select, based on the data corresponding to the individual, one of the plurality of intent identification models;
identify, using the selected one of the plurality of intent identification models, an intent of the individual;
select, based on the intent of the individual, one or more engagement output generation models;
generate, using the selected one or more engagement output generation models, a customer engagement output;
identify, using one or more communication channel models, a communication channel, wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output; and
send one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel, the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models, wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format;
continuously train the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual, wherein training of the one or more intent orchestration models comprises training one or more supervised learning models to automatically assemble a labelled dataset of the historical data by initially inputting a manually labelled dataset into the one or more intent orchestration models and automatically generating the labelled dataset as a function of the manually labelled dataset, such that the one or more intent orchestration models compare the labelled dataset to a real-time dataset comprising the post-historical data to identify the intent of the individual;
wherein generation of the customer engagement output comprises comparison of the labelled dataset of the historical data to the real-time dataset to further identify the intent of the individual based on the real-time dataset.